Abstract
Solar photovoltaic (PV) power generation has become one of the most widely adopted forms of clean energy worldwide. In large-scale PV farm operation and maintenance, unmanned aerial vehicles equipped with thermal infrared (TIR) cameras are increasingly used to enable automated fault detection and classification. However, the long imaging distance and the inherently low resolution of TIR images often lead to fault patterns appearing with low contrast, making subtle discriminative features difficult to extract and posing significant challenges to achieving highly accurate fault identification and classification. To address these challenges, we propose GEPFNet, a network that exploits Group Equivariant Convolutions to explicitly model the geometric structures of faults, incorporates multi-scale processing with unified local-global contextual representations, and adopts a parallel feature fusion strategy to integrate multi-level features and enhance contextual utilization effectively. The design of feature extraction and fusion mechanisms ensures the proposed GEPFNet achieves strong robustness and generalization under complex operational conditions. The effectiveness of GEPFNet was validated on two public datasets with distinct resolutions, class distributions, and feature characteristics: PVF-10 and the Infrared Solar Module (ISM) dataset. Extensive experiments and statistical analyses demonstrate that the proposed GEPFNet achieves state-of-the-art performance on the PVF-10 dataset, obtaining an accuracy of 96.05 %+/- 0.42 for the 2-Class task and 94.64 %+/- 0.35 for the 10-Class task. On the ISM dataset, GEPFNet achieves an improvement of approximately 5 % over the baseline models. Moreover, under highly imbalanced data distributions, the proposed GEPFNet achieves average accuracy improvements of 5.83% and 3.82% on PVF-10 and ISM, respectively, further demonstrating its capability to enhance class-wise performance. With only 9.51 GFLOPs, GEPFNet also exhibits notable computational efficiency, making it well suited for PV fault classification in TIR imagery.

Abstract
Ultra-wideband (UWB) systems are critical for indoor positioning in robotics, industrial tracking, and asset management due to their accuracy in multipath-prone environments. Like GPS satellites requiring precise orbital data, UWB systems depend on well-calibrated anchors-fixed reference points whose positional accuracy directly impacts location estimates. We systematically evaluate and compare computational calibration methods, such as Genetic Algorithms, Maximum Likelihood, and the Extended Kalman Filter, using synthetic data, assessing both efficiency and error reduction in calibration and location. Nonlinear Least Squares (NLS) outperformed other approaches from this review as well as state-of-the-art methods, reducing anchor calibration errors to 10.7cm (86.03% improvement from 1-meter initial uncertainty) and tag localization errors to 5.6cm (88.35% reduction). NLS maintained computational efficiency (mean execution time of 0.011s, proving ideal for real-world deployments where efficiency and accuracy are critical.